Abstract
Automatic question generation is a challenging task [11] that aims to generate questions from plain texts, and has been widely and actively researched in various fields. Generated questions can be used for educational purposes, largely for mid-terms, final exams, and also for pop quizzes. In this paper, we propose a novel similarity-based multiple choice question generation model without any pre-knowledge or additional dataset.
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Notes
- 1.
Due to page limitation, we refer readers to [5] for further detail.
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Park, J., Cho, H., Lee, Sg. (2018). Automatic Generation of Multiple-Choice Fill-in-the-Blank Question Using Document Embedding. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_48
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DOI: https://doi.org/10.1007/978-3-319-93846-2_48
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